Reinforced concrete bridge damage detection using arithmetic optimization algorithm with deep feature fusion

Majdy M. Eltahir; Ghadah Aldehim; Nabil Sharaf Almalki; Mrim M. Alnfiai; Azza Elneil Osman; Majdy M. Eltahir; Ghadah Aldehim; Nabil Sharaf Almalki; Mrim M. Alnfiai; Azza Elneil Osman

doi:10.3934/math.20231499

AIMS Mathematics

2023, Volume 8, Issue 12: 29290-29306. doi: 10.3934/math.20231499

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Reinforced concrete bridge damage detection using arithmetic optimization algorithm with deep feature fusion

1.
Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
2.
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
3.
Department of Special Education, College of Education, King Saud University, Riyadh 12372, Saudi Arabia
4.
Department of Information Technology, College of Computers and Information Technology, Taif University, Taif P.O. Box 11099, Taif, 21944, Saudi Arabia
5.
Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

Received: 20 August 2023 Revised: 22 September 2023 Accepted: 06 October 2023 Published: 27 October 2023
MSC : 11Y40

Inspection of Reinforced Concrete (RC) bridges is critical in order to ensure its safety and conduct essential maintenance works. Earlier defect detection is vital to maintain the stability of the concrete bridges. The current bridge maintenance protocols rely mainly upon manual visual inspection, which is subjective, unreliable and labour-intensive one. On the contrary, computer vision technique, based on deep learning methods, is regarded as the latest technique for structural damage detection due to its end-to-end training without the need for feature engineering. The classification process assists the authorities and engineers in understanding the safety level of the bridge, thus making informed decisions regarding rehabilitation or replacement, and prioritising the repair and maintenance efforts. In this background, the current study develops an RC Bridge Damage Detection using an Arithmetic Optimization Algorithm with a Deep Feature Fusion (RCBDD-AOADFF) method. The purpose of the proposed RCBDD-AOADFF technique is to identify and classify different kinds of defects in RC bridges. In the presented RCBDD-AOADFF technique, the feature fusion process is performed using the Darknet-19 and Nasnet-Mobile models. For damage classification process, the attention-based Long Short-Term Memory (ALSTM) model is used. To enhance the classification results of the ALSTM model, the AOA is applied for the hyperparameter selection process. The performance of the RCBDD-AOADFF method was validated using the RC bridge damage dataset. The extensive analysis outcomes revealed the potentials of the RCBDD-AOADFF technique on RC bridge damage detection process.
- sustainability,
- RC bridge,
- damage detection,
- arithmetic optimization algorithm,
- feature fusion
Citation: Majdy M. Eltahir, Ghadah Aldehim, Nabil Sharaf Almalki, Mrim M. Alnfiai, Azza Elneil Osman. Reinforced concrete bridge damage detection using arithmetic optimization algorithm with deep feature fusion[J]. AIMS Mathematics, 2023, 8(12): 29290-29306. doi: 10.3934/math.20231499

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Abstract

Inspection of Reinforced Concrete (RC) bridges is critical in order to ensure its safety and conduct essential maintenance works. Earlier defect detection is vital to maintain the stability of the concrete bridges. The current bridge maintenance protocols rely mainly upon manual visual inspection, which is subjective, unreliable and labour-intensive one. On the contrary, computer vision technique, based on deep learning methods, is regarded as the latest technique for structural damage detection due to its end-to-end training without the need for feature engineering. The classification process assists the authorities and engineers in understanding the safety level of the bridge, thus making informed decisions regarding rehabilitation or replacement, and prioritising the repair and maintenance efforts. In this background, the current study develops an RC Bridge Damage Detection using an Arithmetic Optimization Algorithm with a Deep Feature Fusion (RCBDD-AOADFF) method. The purpose of the proposed RCBDD-AOADFF technique is to identify and classify different kinds of defects in RC bridges. In the presented RCBDD-AOADFF technique, the feature fusion process is performed using the Darknet-19 and Nasnet-Mobile models. For damage classification process, the attention-based Long Short-Term Memory (ALSTM) model is used. To enhance the classification results of the ALSTM model, the AOA is applied for the hyperparameter selection process. The performance of the RCBDD-AOADFF method was validated using the RC bridge damage dataset. The extensive analysis outcomes revealed the potentials of the RCBDD-AOADFF technique on RC bridge damage detection process.

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